Short-term Voltage Instability Research Articles

Short-term voltage instability prediction using pre-identified voltage templates and machine learning classifiers

System operators use different procedures to detect disturbances which can lead to short-term voltage instability. These schemes are designed using the domain knowledge and generally specific for the corresponding system. The design procedure of such schemes is heuristic and cannot be directly applied to any other power system. This paper proposes a data-driven approach to predict short-term voltage stability using set of post-disturbance voltage templates and a machine learning-based classifier. In this scheme, a set of voltage templates corresponding to stable/unstable events is identified for selected buses using a suitably generated training data set. The proximity of real-time voltage trajectory to each of the template is computed and input to a trained machine learning classifier to predict the short-term voltage stability status. The proposed approach is validated using IEEE Nordic test system. The investigations show that this method provides accurate short-term voltage stability predictions. Furthermore, impact of adverse phenomena such as measurement noise and missing data on the proposed method is analyzed to confirm the robustness of this scheme in particle environments. © 2017 Elsevier Inc. All rights reserved.

A Novel Data-Driven Approach for Managing Renewable Energy Systems During Short-Term Voltage Instability

A Novel Data-Driven Approach for Managing Renewable Energy Systems During Short-Term Voltage Instability

Deep learning based on Transformer architecture for power system short-term voltage stability assessment with class imbalance

Most existing data-driven power system short-term voltage stability assessment (STVSA) approaches presume class-balanced input data. However, in practical applications, the occurrence of short-term voltage instability following a disturbance is minimal, leading to a significant class imbalance problem and a consequent decline in classifier performance. This work proposes a Transformer-based STVSA method to address this challenge. By utilizing the basic Transformer architecture, a stability assessment Transformer (StaaT) is developed as a classification model to reflect the correlation between the operational states of the system and the resulting stability outcomes. To combat the negative impact of imbalanced datasets, this work employs a conditional Wasserstein generative adversarial network with gradient penalty (CWGAN-GP) for synthetic data generation, aiding in the creation of a balanced, representative training set for the classifier. Semi-supervised clustering learning is implemented to enhance clustering quality, addressing the lack of a unified quantitative criterion for short-term voltage stability. Numerical tests on the IEEE 39-bus test system extensively demonstrate that the proposed method exhibits robust performance under class imbalances up to 100:1 and noisy environments, and maintains consistent effectiveness even with an increased penetration of renewable energy. Comparative results reveal that the CWGAN-GP generates more balanced datasets than traditional oversampling methods and that the StaaT outperforms other deep learning algorithms. This study presents a compelling solution for real-world STVSA applications that often face class imbalance and data noise challenges.

Encoding Time Series as Images: A Robust and Transferable Framework for Power System DIM Identification Combining Rules and VGGNet

Digital simulation is very important for the safe and economic operation of power systems. Power system dominant instability mode (DIM) identification is an intractable problem in large-scale transient simulation analysis, because transient instability and short-term voltage instability are often intertwined. Deep learning is a promising way to achieve accurate DIM identification. By encoding time series as images, this paper proposes a robust and transferable DIM identification framework combining curve filtering rules and a dual-channel VGGNet (DCVGG) image recognition network. The curve filtering rules can effectively reduce the redundancy of massive time-domain features, the model parameters, and training time. A skillfully designed method is proposed to encode time series as images and improve the feature extraction capability of convolutional neural networks. The proposed DCVGG network is capable of processing rotor angle and voltage images simultaneously and has the potential to become a pre-trained model, making transfer learning between different power systems possible. Case studies conducted on 8-machine 36-bus system and Northeast China Power Grid demonstrate the proposed framework has better performance, scalability, robustness, and transferable ability than contrastive machine learning methods.

Deep Reinforcement Learning for Load Shedding Against Short-Term Voltage Instability in Large Power Systems.

We introduce an innovative solution approach to the challenging dynamic load-shedding problem which directly affects the stability of large power grid. Our proposed deep Q-network for load-shedding (DQN-LS) determines optimal load-shedding strategy to maintain power system stability by taking into account both spatial and temporal information of a dynamically operating power system, using a convolutional long-short-term memory (ConvLSTM) network to automatically capture dynamic features that are translation-invariant in short-term voltage instability, and by introducing a new design of the reward function. The overall goal for the proposed DQN-LS is to provide real-time, fast, and accurate load-shedding decisions to increase the quality and probability of voltage recovery. To demonstrate the efficacy of our proposed approach and its scalability to large-scale, complex dynamic problems, we utilize the China Southern Grid (CSG) to obtain our test results, which clearly show superior voltage recovery performance by employing the proposed DQN-LS under different and uncertain power system fault conditions. What we have developed and demonstrated in this study, in terms of the scale of the problem, the load-shedding performance obtained, and the DQN-LS approach, have not been demonstrated previously.

Load shedding method aimed fast voltage recovery to prevent interference of FIDVR with UV relays

AbstractThe fault‐induced delayed voltage recovery (FIDVR) and short‐term voltage instability (STVI) phenomena appear in networks with high penetration of induction motor loads because the increase in requested reactive powers of these loads prevents the voltages from quickly returning to their pre‐fault levels. Load shedding (LS) is one of the ways to deal with FIDVR and STVI and reduce the imbalance between the generation and demand of reactive power. Under‐voltage (UV) relays that disconnect loads during voltage drop cannot effectively deal with this phenomenon because of their inability to detect the effective loads on the reduction of FIDVR and STVI severities. Therefore, the operation of UV relays during these phenomena causes unnecessary load disconnection, and the interference of their operations with FIDVR and STVI must be avoided. This paper presents two wide‐area approaches based on network and loads parameters, the first of which deals with the most critical FIDVR, and the second tries to simultaneously handle critical FIDVRs of all buses. Also, bus prioritization for LS during STVI has been addressed. Simulation results revealed the better performance of the proposed approaches than the previous ones in terms of the amount of LS and the number of selected buses for LS.

A Data-Driven Method for Power System Emergency Control to Improve Short-Term Voltage Stability

The power system contains a variety of uncertainties of different types of sources and loads, as well as random contingencies. Under these uncertainties and rapidly changing operating conditions, traditional rule-based methods cannot dynamically handle short-term voltage instability. To alleviate this situation, this paper proposes a novel power system emergency control scheme using a data-driven method, which combines edge-conditioned graph convolutional networks and deep reinforcement learning. The edge-conditioned graph convolutional network is utilized to extract the characteristics from not only power system nodes but also transmission lines. Deep reinforcement learning is introduced to perform load shedding actions, so as to guarantee the safety and stability of the electric power system. The IEEE 39-bus network is utilized for simulations to validate the effectiveness of the proposed data-driven method. The outcomes demonstrate the proposed method can generate a superior strategy in a number of the short-term voltage instability(STVI) circumstances.

Real-Time Adaptive UVLS by Optimized Fuzzy Controllers for Short-Term Voltage Stability Control

This paper proposes a novel real-time adaptive under voltage load shedding (UVLS) method by using optimized fuzzy logic controllers to address fault-induced delayed voltage recovery (FIDVR) and short-term voltage instability (STVIS) problems. An intelligent multi-input multi-output fuzzy logic load shedding controller is developed based on a short-term voltage recovery criterion (STVRC). For the controller, the magnitude deviation and the recovery time deviation of voltage trajectory are extracted as inputs, while the load shedding amount at the current load shedding stage and the time delay of the next stage are taken as outputs. Particle swarm optimization (PSO) technique is further employed for the tuning of the controller parameters to realize the voltage recovery requirement at the lowest cost of load shedding. The proposed UVLS method is formed by a group of the fuzzy logic controllers deployed at the load buses, each monitoring its own bus voltage and shedding load in closed-loop. Responding to the severity of voltage variations, the proposed method is able to adaptively determine the location, time and amount of load shedding in real-time to make the voltage satisfy the STVRC. The effectiveness of the proposed method compared to other methods is testified by simulations on the Nordic test system.

A graph attention networks-based model to distinguish the transient rotor angle instability and short-term voltage instability in power systems

Digital simulation is significant for the operating mode and control decision-making of power systems. In the process of simulation data analysis, stability analysis is an essential part. One of the most challenging tasks is to distinguish between transient rotor angle instability and short-term voltage instability. This paper proposes a graph attention networks (GATs)-based method to overcome this ticklish problem via integrating power grid topology information into the neural networks. Compared with the conventional graph convolutional networks (GCNs), the attention mechanism is introduced into the GATs to learn the weights among different neighbor vertices in the graph. Due to the difficulty of distinguishing between the rotor angle instability and voltage instability in some samples, a label-smoothing method is adopted to alleviate the influence caused by label inaccuracy. Case studies are conducted on an 8-machine 36-bus system and Northeast China Power System. Simulation results show that the proposed method has better performance than conventional GCNs and other machine learning methods.

Fast online decision tree-based scheme for predicting transient and short-term voltage stability status and determining driving force of instability

This paper presents a novel fast machine learning-based scheme for predicting the transient and short-term voltage (STV) stability status and determining the driving force of instability, without any need to measure post-fault-clearance data. In the proposed scheme, by employing decision tree (DT) classifier and using the measurements of the installed phasor measurement unit (PMU) at the generator and the induction motor (IM) buses, first, the stability status of either transient or STV (including the sustained low voltage without recovery and the voltage collapse) is predicted just after disturbance in Wide Area Measurement System (WAMS) framework. Then, for unstable cases, based on transient and STV stability mechanisms, the driving force of the instability is determined between transient and STV instability. In order to evaluate the performance of the proposed scheme, the method has been tested on IEEE 39-bus test system and IEEE Nordic test system. The simulation results show the proposed scheme is highly accurate and timely.

An agent‐based fast concurrent prediction scheme for transient and short‐term voltage instability

A novel agent-based online prediction method is presented in this paper, to predict the status of both transient and short-term voltage (STV) stability, against fault occurrence. In the proposed method, the trajectories of the relative frequency deviation (ΔF) and the power imbalance (ΔP) are estimated by employing the third-degree polynomial curve fitting method. By tracking the estimated trajectories on ΔF–ΔP plane and checking some simple defined rules, an early prediction of both transient and STV instability is achieved in an organized multi-agent system (MAS). In order to evaluate the performance of the proposed algorithm, the method has been tested on IEEE 39-bus system, IEEE 118-bus system and IEEE Nordic test system. Based on the obtained results, the proposed algorithm has an overall accuracy of 99.5% under symmetrical and asymmetrical faults, PMU measurement error, different operating points and topological changes.

Short-Term Voltage Stability-Constrained Unit Commitment for Receiving-End Grid With Multi-Infeed HVDCs

With the increasing development of AC/DC hybrid power systems, local synchronous generators in receiving-end grids are being replaced by high-voltage direct current (HVDC) power. The risk of short-term voltage instability exists because of the decline in dynamic reactive power resources from generators, the presence of high-proportioned induction motors and the demand for vast reactive power of HVDC under AC faults. In this situation, unit commitment (UC) should take short-term voltage stability into account. Hence, crucial generators can be retained to provide sufficient dynamic reactive power. This paper proposes a UC model incorporating short-term voltage stability constraints (SVSCUC). Furthermore, a decomposition-based solution approach is presented, which involves an alternate iterative solving of UC and differential-algebraic equations (DAEs). Time-domain simulation (TDS) is utilized to quantify the stability with an indicator. Linear stable cuts are formulated through trajectory sensitivity analysis and added into the UC. The UC is decomposed into a master problem based on mixed-integer linear programming (MILP) and subproblems. The SVSCUC is illustrated using benchmark systems and a real system in eastern China. The results show the effectiveness and efficiency of the proposed methods. Violations of short-term voltage stability for credible contingencies are eliminated under the commitment of SVSCUC.

A Grid-Support Strategy With PV Units to Boost Short-Term Voltage Stability Under Asymmetrical Faults

Modern grid codes demand the integration of voltage support capability with photo-voltaic (PV) generators to ensure a secure operation of power systems. On the other hand, short-term voltage instability (STVI) of distribution networks (DNs) is one of the key issues to be addressed. It can occur due to high proportion of induction motor loads. However, the existing literature lacks a thorough analysis of short-term voltage stability (STVS) of DNs following an asymmetrical fault. Furthermore, an effective voltage-support strategy for PV units, which can improve the STVS while mitigating the excessive voltage swell, is essential for secure operation. Hence, firstly this paper thoroughly investigates the STVS of DNs subjected to asymmetrical faults. It is perceived that voltage support through conventional methods can increase the risk of STVI and excessive voltage swell. Secondly, a new voltage-support strategy is proposed based on the negative sequence voltage at point of common coupling (PCC) aimed at mitigating the STVI and regulating the phase voltages within the margins. The key features of the proposed strategy are, fast and accurate estimation of the network's impedance at PCC is not required and it can be easily re-designed considering the network's dynamic behaviors. The efficacy of the proposed method is validated on two IEEE benchmark test systems, and the results demonstrate the effectiveness in improving the STVS and alleviating over voltage issues.

Short-Term Voltage Stability Improvement via Dynamic Voltage Support Capability of ESS Devices

Modern power systems are undergoing a steady increase in the loading level and higher penetration of renewables. Induction motor (IM) loads and fixed-speed induction generator (FSIG) based wind turbines (WTs) are broadly used in many power systems. Both IMs and FSIGs draw high amounts of reactive power during large disturbances, which pose major problems for short-term voltage stability (ST-VS). In this paper, the effects of IMs, FSIG-based WTs, and over-excitation limiter (OEL) of synchronous generators on ST-VS are analyzed and illustrated. Then, the use of energy storage systems (ESSs) to provide dynamic voltage support (DVS) to power system during and after large disturbances is investigated, as a countermeasure against short-term voltage instability. To implement ESS's DVS capability, the required power injection by the ESS is dynamically determined and then tracked after the occurrence of a disturbance. The effects of implementing fault ride through and time-overload capabilities of ESS's inverter, on ST-VS of the system, are also analyzed. Optimal power factor adjustment for power injection by ESS, from an ST-VS viewpoint, is discussed as well. It is shown that the DVS capability of an ESS can be effectively used against detrimental effects of IMs, FSIGs, and OELs on ST-VS.

Short-Term Voltage Stability Enhancement in Residential Grid With High Penetration of Rooftop PV Units

Short-term voltage instability (STVI) imposes a severe threat to modern distribution networks (DNs) where a large number of intermittent distributed generator (DG) units, like rooftop photovoltaic (PV), is being integrated. Consequently, most of the international standards have been revised by incorporating the requirement of the dynamic voltage support (DVS) through DG units, which is a promising approach to alleviate the STVI. In this paper, a novel DVS strategy is proposed to improve the short-term voltage stability (STVS) in residential grids. In comparison with other DVS strategies, the proposed DVS scheme maximizes the active power support from PV units following a contingency utilizing the maximum allowable current of the PV inverters. Moreover, the inverter design margin is taken into account in designing the proposed scheme to limit the injected grid current within the maximum allowable inverter current. The impact of the inverter design margin on the STVS is explained, and the effectiveness of the proposed strategy compared with the conventional DVS is demonstrated. The feasibility of the DVS control strategies in practical application is studied. Several case studies are carried out on benchmark IEEE 4 bus and IEEE 13 node test feeder systems, and finally, on a ring-type DN. The results show that the proposed DVS scheme is feasible, and achieved superior performance compared to the other strategies. Furthermore, it has been shown that implementation of the proposed DVS scheme can avoid the installation of an expensive 1200-kVA D-STATCOM for STVS improvement in the target system.

Active and Reactive Power Joint Balancing for Analyzing Short-Term Voltage Instability Caused by Induction Motor

Short-term voltage instability has a sensational effect once it occurs with massive loss of load, possibly area instability, and voltage collapse. This paper analyzes the short-term voltage instability caused by induction motor from the viewpoint of active and reactive power joint balancing. The analysis method is based on (1) the reactive power balancing between system supply and induction motor demand, and (2) the active power balancing between air-gap power and mechanical power, which is expressed by the region of rotor acceleration and deceleration in the Q-V plane. With the region of rotor acceleration and deceleration in the Q-V plane and the reactive power balancing, the movement direction of the operating point can be visually observed in the Q-V plane, thereby achieving a clear comprehension of physical properties behind the short-term voltage instability phenomenon. Furthermore, the instability mechanisms of two kinds of grid-connected induction motor operation conditions after a large disturbance are discussed to explain the basic theory of the analysis method and to provide examples of its application. Time-domain simulations are presented for a single-load infinite-bus system to validate the analyses.

A model and data hybrid-driven short-term voltage stability real-time monitoring method

This paper proposes a model and data hybrid-driven short-term voltage stability assessment method. First, a double-generator-one-load (DGOL) simplified model for short-term voltage stability analysis is introduced, and the mechanism of the occurrence of short-term voltage instability is analysed based on a DGOL model. Then, a model and data hybrid-driven online application algorithm is proposed, which includes a pre-fault analysis module and a real-time monitoring module. At last, a two-dimensional index is proposed to evaluate the short-term voltage stability margin. Simulation work is first conducted based on the DGOL system to illustrate the advantage of the proposed simplified model, and on the EPRI 36-bus system to demonstrate the effectiveness of online application algorithm.

Practical short‐term voltage stability index based on voltage curves: definition, verification and case studies

Short-term voltage stability (SVS) is a serious issue in modern power systems. In China, the East China Power Network is especially vulnerable to short-term voltage instability due to its increasing dependence on electrical power from external through high-voltage direct current (HVDC) transmission lines. To study the SVS, a criterion/index is first required to evaluate the SVS of power systems. However, the currently used practical criteria cannot effectively evaluate the influence of controlling strategies (such as regulating dynamic VAR reservation) of power systems. Therefore, a practical and continuous SVS index (SVSI) based on voltage curves is proposed in this study. The proposed SVSI can quantify the extent of SVS in power systems, thus can be used in the optimisation target of preventive control for improving the SVS of power systems. Moreover, the SVSI consists of three components, and each component reflects a feature of the voltage curve in terms of SVS. The SVSI is essentially a model-free method for assessing the SVS of power systems, so it is robust. Three verifying cases and one application case are presented to show the validity of the SVSI and the feasibility of the SVSI in practical applications.

Multi-Phase Under Voltage Load Shedding Scheme for Preventing Delayed Voltage Recovery by Induction Motor Power Consumption Characteristics

This paper aims to develop a multi-phase under voltage load shedding (MUVLS) strategy that effectively sheds the load to mitigate delayed voltage recovery (DVR). The major cause of the DVR phenomenon is related to the dynamic characteristics of induction motor (IM) loads. The deaccelerating and stalling of the IM load during disturbances is the main driving force of short-term voltage instability, resulting in an amount of reactive power consumption and excessive current draw. With the economic efficiency of energy use, the proportion of IM loads is gradually increasing, and this trend might deteriorate system stability. This paper focuses on the impact of IM loads in the Korean power system and analyzes the parameter sensitivity of IM loads and the proportion of appropriate IM loads. The proposed procedure for under voltage load shedding (UVLS) applies voltage stability criteria to decide the most efficient load shedding scheme. The determined MUVLS scheme can offer new and more effective remedial actions to maintain voltage stability, considering the characteristics of IM loads. Case studies on the Korean power system have validated the performance of the proposed MUVLS scheme under severe contingency scenarios, showing that the proposed strategy effectively mitigates DVR.

Dynamic Voltage Support by TL-PV Systems to Mitigate Short-Term Voltage Instability in Residential DN

Recently, transformerless (TL) inverters are being extensively used in small-scale photovoltaic (PV) systems due to their compact-size, lighter-weight, lower-cost and higher-efficiency compared to their counterpart with transformer. However, the growing penetration of these small-scale based PV systems and low-inertia induction motor (IM) loads in low-voltage distribution networks (DNs) make the grid more vulnerable to short-term voltage stability (STVS). Hence, this paper thoroughly examines the STVS of DN with high-penetration of TL-PV units, and provides countermeasures by TL-PV systems to mitigate any short-term voltage instability. The detailed dynamic model of the TL inverter is developed first. Next, three control strategies: (1) constant peak current (CPC), (2) constant active current (CAC), and (3) constant active power (CAP) with low voltage ride through (LVRT) and dynamic voltage support (DVS) capabilities are proposed to improve STVS. The impacts of different level of PV penetrations and LVRT capability of TL-PV inverters on the STVS are explored. Moreover, countermeasures, such as LVRT with CAC and CAP controls and DVS by the TL-PV systems are designed and implemented. Several case studies are carried out on an IEEE 4 bus system first, and later extended to IEEE 13 node test feeder. The results show that DVS can further improve STVS in residential DNs. In addition, CAP and CAC control strategies can speed-up the postdisturbance voltage recovery compared with the conventional CPC control.